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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張智星 | zh_TW |
dc.contributor.advisor | Jyh-Shing Roger Jang | en |
dc.contributor.author | 林育駿 | zh_TW |
dc.contributor.author | Yu-Chun Lin | en |
dc.date.accessioned | 2023-09-22T16:31:27Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-09-22 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-12 | - |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89881 | - |
dc.description.abstract | 台語語音辨識主要面對問題分為: 1. 缺乏大量且公開的台語語料集,2. 台語文字書寫系統不統一,前者導致進行語音辨識的任務上面臨資料不足,後者造成輸出格式不統一且難以讀解。本研究以台語語音辨識結合中文翻譯為任務,透過預訓練語音模型結合端到端深度學習模型的架構,建立台語語音翻譯模型。以少量台語語音配對中文文本語料為基礎,透過大量蒐集網路台語語音資料進行半監督式學習,並設計資料清洗演算法,改善台語語音翻譯系統以及台語語料。研究探討主要分為端到端語音翻譯模型、預訓練語音模型特徵、疊代訓練方法以及語料清洗四種改進方向。根據實驗結果,驗證上述方法皆能有效改善台語語音翻譯中文的表現。 | zh_TW |
dc.description.abstract | The challenges in Taiwanese speech recognition can be primarily categorized into two aspects: 1) the lack of abundant and publicly available Taiwanese speech corpora, and 2) the inconsistency in the written system of Taiwanese. The former results in insufficient data for speech recognition tasks, while the latter leads to inconsistent output formats and difficulties in interpretation. In this study, we focus on the task of combining Taiwanese speech recognition with Chinese translation and propose a framework that integrates pretrained speech models with end-to-end deep learning models to build a Taiwanese speech translation system. Based on a limited amount of Taiwanese speech-Chinese text paired data, we utilize semi-supervised learning through a large collection of Taiwanese speech data gathered from the internet and design data cleaning algorithms to improve both the Taiwanese speech translation system and the Taiwanese speech corpora. The research explores four main improvement directions: end-to-end speech translation models, pretrained speech model features, iterative training methods, and data cleaning. Experimental results validate the effectiveness of the aforementioned approaches in improving the performance of Taiwanese speech translation to Chinese. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T16:31:27Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-09-22T16:31:27Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 致謝 i
摘要 iii Abstract v 目錄 vii 圖目錄 xi 表目錄 xiii 第一章 緒論 1 1.1 研究動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究貢獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 章節概述 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 第二章 相關文獻 5 2.1 機器翻譯與語音辨識 . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 機器翻譯 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1.1 基於規則條件的機器翻譯 . . . . . . . . . . . . . . . 5 2.1.1.2 統計機器翻譯 . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1.3 神經機器翻譯 . . . . . . . . . . . . . . . . . . . . . . 8 2.1.2 語音辨識 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.2.1 序列到序列模型 . . . . . . . . . . . . . . . . . . . . 9 2.1.2.2 連接時序分類 (CTC) . . . . . . . . . . . . . . . . . . 10 2.1.2.3 RNN-Transducer . . . . . . . . . . . . . . . . . . . . 11 2.1.2.4 LAS . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.3 語音翻譯 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 語音特徵 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1 對數梅爾頻譜圖 (Log-mel spectrogram) . . . . . . . . . . . . . . 14 2.2.2 梅爾頻率倒譜系數 (Mel-frequency cepstral coefficient, MFCC) . . 14 2.2.3 自監督語音模型特徵 . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 低資源語言相關研究 . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.1 自監督語音模型提取語音特徵 . . . . . . . . . . . . . . . . . . . 16 2.3.2 半監督學習訓練方法 . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.3 資料擴增 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 第三章 資料集和任務介紹 19 3.1 資料集介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.1 台語資料集 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.1.1 TAT 資料集 . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.1.2 TAI YouTube 資料集 . . . . . . . . . . . . . . . . . . 20 3.1.2 英文資料集 LibriSpeech . . . . . . . . . . . . . . . . . . . . . . . 21 3.1.3 中文資料集 Common Voice Chinese . . . . . . . . . . . . . . . . . 21 3.2 任務介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2.1 台語語音翻譯任務 . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 正規化方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.3 評量指標 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2.3.1 字錯誤率 (Character error rate, CER) . . . . . . . . . 25 3.2.3.2 雙語替換評測 (bilingual evaluation understudy, BLEU) 25 第四章 研究方法 27 4.1 端到端台語語音翻譯系統 . . . . . . . . . . . . . . . . . . . . . . . . 27 4.1.1 WAV2VEC 2.0 進行特徵抽取 . . . . . . . . . . . . . . . . . . . . 29 4.1.2 Conformer 端到端語音翻譯模型 . . . . . . . . . . . . . . . . . . 29 4.2 預訓練語音模型微調 . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.3 半監督式疊代訓練 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.4 語料清洗系統 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.4.1 標註語料清洗 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.4.2 文本處理 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.4.2.1 語言模型過濾器 (LM filter) . . . . . . . . . . . . . . 34 4.4.2.2 語速過濾器 (SR filter) . . . . . . . . . . . . . . . . . 35 4.4.3 語音處理 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.4.3.1 語音活性偵測方法 . . . . . . . . . . . . . . . . . . . 36 4.4.3.2 語言辨識方法 . . . . . . . . . . . . . . . . . . . . . . 37 第五章 實驗設計與結果討論 39 5.1 實驗路線圖與實驗設定 . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.1.1 實驗路線圖 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.1.2 實驗設定 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.1.2.1 訓練設備環境 . . . . . . . . . . . . . . . . . . . . . . 40 5.1.2.2 實驗參數設定 . . . . . . . . . . . . . . . . . . . . . . 41 5.2 端到端語音翻譯模型 . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.2.1 模型訓練與錯誤分析 . . . . . . . . . . . . . . . . . . . . . . . . 42 5.2.1.1 錯誤分析 . . . . . . . . . . . . . . . . . . . . . . . . 42 5.2.1.2 評量指標分析 . . . . . . . . . . . . . . . . . . . . . . 44 5.2.2 下游模型比較 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.2.3 語音特徵比較 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.3 自監督式預訓練語音模型 . . . . . . . . . . . . . . . . . . . . . . . . 49 5.3.1 預訓練語音模型比較 . . . . . . . . . . . . . . . . . . . . . . . . 49 5.3.2 台語預訓練語音模型微調 . . . . . . . . . . . . . . . . . . . . . . 50 5.4 半監督學習疊代改善 . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.4.1 無標註語料擴增 . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.5 資料清洗流程改善 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.5.1 標註語料清洗 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.5.2 資料清洗流程: 文本處理 . . . . . . . . . . . . . . . . . . . . . . 54 5.5.3 資料清洗流程: 語音處理 . . . . . . . . . . . . . . . . . . . . . . 55 5.6 實驗總結與比較 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 第六章 結論與未來工作 61 6.1 結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.2 未來工作 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 參考文獻 65 | - |
dc.language.iso | zh_TW | - |
dc.title | 通過半監督學習改進端到端台語至中文語音翻譯 | zh_TW |
dc.title | Improving End-to-end Taiwanese-to-Chinese Speech Translation by Semi-supervised Learning | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 王新民;廖元甫 | zh_TW |
dc.contributor.oralexamcommittee | Hsin-Min Wang;Yuan-Fu Liao | en |
dc.subject.keyword | 自動語音辨識,自監督式學習,端到端語音辨識,機器翻譯, | zh_TW |
dc.subject.keyword | Automatic speech recognition,Self-supervised learning,End-to-end speech recognition,Machine translation, | en |
dc.relation.page | 73 | - |
dc.identifier.doi | 10.6342/NTU202301825 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2023-08-13 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 資訊工程學系 | - |
顯示於系所單位: | 資訊工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
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ntu-111-2.pdf 此日期後於網路公開 2028-08-08 | 2.8 MB | Adobe PDF |
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